pipewise 1.1.0

A lightweight pandas DataFrame pipeline with schema validation and rollback support.

Pipewise

Pipewise is a lightweight pandas.DataFrame pipeline library for teams that want reusable data-processing steps without adopting a heavyweight workflow framework.

It helps you:

• register step functions with decorators
• map function arguments to DataFrame columns automatically
• write results back to one or more columns
• validate input and output schema rules
• fall back from vectorized mode to row-wise mode when needed
• roll back all changes if any task fails

Installation

After publishing to PyPI:

pip install pipewise

For local development:

pip install -r requirements.txt
pip install -e .

Quick Start

import pandas as pd

from pipewise import Pipewise

df = pd.DataFrame({"a": [1, 2, 3], "b": [10, 20, 30]})
pipewise = Pipewise(df)


@pipewise.register(outputs=["sum", "product"])
def calc(a, b):
    return a + b, a * b


result = pipewise.run()
print(result)

Core Features

• Sequential pipeline registration with @pipewise.register(...)
• Multiple output modes:
  • no output
  • single-column output
  • multi-column output
  • typed output mapping
  • dynamic dict output
• Grouped execution with groupby
• Schema checks:
  • dtype
  • nullable
  • allowed_values
  • min
  • max
• Automatic rollback when execution fails
• Custom exception hierarchy for easier debugging

Package Structure

pipewise/
  __init__.py
  core.py
  errors.py
tests/
  test_pipewise.py
README.md
requirements.txt
pyproject.toml

Public Metadata

from pipewise import __author__, __version__

• __version__ = "1.0.1"
• __author__ = "XiaoZhouZhou"

Testing

python -m unittest -v tests/test_pipewise.py

Publish Checklist

1. Update pipewise/__init__.py version.
2. Update pyproject.toml version if needed.
3. Run tests.
4. Build the package:

python -m build

5. Upload to PyPI with your preferred workflow, for example twine.